Machine Learning-Enhanced T Cell Neoepitope Discovery for Immunotherapy Design

Abstract

Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases.

Tumor neoantigens: from basic research to clinical applications

“…Less than 3% were reported to elicit the T cell response [53]. One major reason may be that the machine learning algorithms are highly dependent on the datasets available for training and testing [54]. As a widely used resource, The Immune Epitope Database and Analysis Resource (IEDB) hosts a database of experimentally validated epitopes.…”

Artificial Intelligence in Cancer Research

“…From the images which are obtained from CT and MRI as well as from the tissue slides, AI can help in understanding the recognition pattern [8]. In future AI can help in understanding the tumor-immune interaction, resistance to immunotherapy, and mechanistic perspectives of combination therapy [9]. Machine intelligence can also have a wider scope such as: (i) Therapy response can be monitored by longitudinal noninvasive monitoring, (ii).…”

Applying artificial intelligence for cancer immunotherapy

“…These findings provide powerful proof that AI-based methods might have potential application in the location and identification of abnormal histomorphology patterns in routine whole-slide images of cancer patients. Accordingly, ML techniques based on histopathology analysis could provide new opportunities to predict the response to cancer immunotherapy 73 . Studies have proposed that defective mismatch repair (MMR) machinery caused by mutations in MMR genes that lead to an increasing number of somatic mutations in the genome is significantly related to the ICB response 74 .…”